While researchers agree as to the safety benefits of turbo-roundabouts, their improvements in terms of capacity and delay remain open to discussion. This is mostly because previous research is based on capacity models that do not fully describe the complex interactions between traffic streams on multi-lane roundabouts. This paper proposes a procedure to calculate capacity based on gap-acceptance theory. It addresses the limitations mentioned by accounting for usually disregarded effects such as the dynamic choice of the entry lane and unequal allocation of traffic in the circulatory lanes. Capacities were calculated for a wide range of demand scenarios and it has been shown that only under demand scenarios that are very specific and uncommon in real-world networks, associated with very high percentages of right-turning entry traffic, can a standard turbo-roundabout be expected to provide more capacity than the equivalent two-lane roundabout. It has also been shown that two-lane roundabouts can normally be expected to provide capacities of 20–30% above those of comparable turbo-roundabouts.
Accessibility is a key factor in defining the quality of life and potential for development of both cities and regions. This article presents a new accessibilitymaximization approach to inter-urban road network long-term planning. The approach is based on a nonlinear combinatorial optimization model. Two heuristics have been developed for solving the model, based on local search and simulated annealing principles, respectively. The efficiency of the heuristics was evaluated on a sample of test problems involving 10-, 20-, and 40-node networks. In the analysis both solution quality and computing effort were taken into account. The approach was used to analyze the ongoing transformation of the Portuguese main road network. The results obtained so far indicate that the model is a valuable decision-aid tool for inter-urban road network long-term planning.
One of the most important tasks in the microscopic simulation of traffic flow, assigned to the car following sub-model, is the modelling of the longitudinal movement of vehicles. The calibration of a car-following model is usually done at an aggregated level, using macroscopic traffic stream variables (speed, flow, density). There is an interest in calibration procedures based on disaggregated data. However, obtaining accurate trajectory data is a real challenge. This paper presents a low-cost procedure to calibrate the Gipps car-following model. The trajectory data is collected with a car equipped with a datalogger and a LIDAR rangefinder. The datalogger combines GPS and accelerometers data to provide accurate speed and acceleration measurements. The LIDAR measures the distances to the leading or following vehicle. Two alternative estimation methods were tested: the first follows individual procedures that explicitly account for the physical meaning of each parameter; the second formulates the calibration as an optimization problem: the objective function is defined so as to minimize the differences between the simulated and real inter-vehicle distances; the problem is solved using an automated procedure based on a genetic algorithm. The results show that the optimization approach leads to a very accurate representation of the specific modeled situation but offers poor transferability; on the other hand, the individual estimation provides a satisfactory fit in a wide range of traffic conditions and hence is the recommended method for forecasting purposes.
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